Package: mlsurvlrnrs 0.0.8

mlsurvlrnrs: R6-Based ML Survival Learners for 'mlexperiments'

Enhances 'mlexperiments' <https://CRAN.R-project.org/package=mlexperiments> with additional machine learning ('ML') learners for survival analysis. The package provides R6-based survival learners for the following algorithms: 'glmnet' <https://CRAN.R-project.org/package=glmnet>, 'ranger' <https://CRAN.R-project.org/package=ranger>, 'xgboost' <https://CRAN.R-project.org/package=xgboost>, and 'rpart' <https://CRAN.R-project.org/package=rpart>. These can be used directly with the 'mlexperiments' R package.

Authors:Lorenz A. Kapsner [cre, aut, cph]

mlsurvlrnrs_0.0.8.tar.gz
mlsurvlrnrs_0.0.8.zip(r-4.7)mlsurvlrnrs_0.0.8.zip(r-4.6)mlsurvlrnrs_0.0.8.zip(r-4.5)
mlsurvlrnrs_0.0.8.tgz(r-4.6-any)mlsurvlrnrs_0.0.8.tgz(r-4.5-any)
mlsurvlrnrs_0.0.8.tar.gz(r-4.7-any)mlsurvlrnrs_0.0.8.tar.gz(r-4.6-any)
mlsurvlrnrs_0.0.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
mlsurvlrnrs/json (API)

# Install 'mlsurvlrnrs' in R:
install.packages('mlsurvlrnrs', repos = c('https://kapsner.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/kapsner/mlsurvlrnrs/issues

On CRAN:

Conda:

algorithmscox-regressionexperimentsglmnetlearnersmachine-learningrandom-survival-forestssurvivalsurvival-support-vector-machinexgboostquarto

5.71 score 3 stars 17 scripts 202 downloads 7 exports 68 dependencies

Last updated from:3da26c3d4b. Checks:8 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK162
source / vignettesOK274
linux-release-x86_64OK166
macos-release-arm64OK150
macos-oldrel-arm64OK150
windows-develOK178
windows-releaseOK170
windows-oldrelOK155

Exports:c_indexLearnerSurvCoxPHCoxLearnerSurvGlmnetCoxLearnerSurvRangerCoxLearnerSurvRpartCoxLearnerSurvXgboostAftLearnerSurvXgboostCox

Dependencies:backportsbase64encbslibcachemcheckmatecliclustercodetoolscolorspacecpp11crayondata.tabledigestdoParallelevaluatefarverfastmapfontawesomeforeachforeignFormulafsggplot2gluegridExtragtablehighrHmischmshtmlTablehtmltoolshtmlwidgetsisobanditeratorsjquerylibjsonlitekdryknitrlabelinglifecyclemagrittrmemoisemimemlexperimentsmllrnrsnnetpkgconfigprettyunitsprogressR6rappdirsRColorBrewerrlangrmarkdownrpartrstudioapiS7sassscalessplitToolsstringistringrtinytexvctrsviridisLitewithrxfunyaml

glmnet: Survival Analysis
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Comparison with Cox Proportional Hazards Regression | Test Fold Equality | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommlsurvlrnrs_glmnet_survival.qmdusingquarto::html

Last update: 2026-01-16
Started: 2024-05-29

rpart: Survival Analysis
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommlsurvlrnrs_rpart_survival.qmdusingquarto::html

Last update: 2026-01-16
Started: 2024-05-29

xgboost: Survival Analysis, AFT Analysis
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommlsurvlrnrs_xgboost_survival_aft.qmdusingquarto::html

Last update: 2026-01-16
Started: 2024-05-29

xgboost: Survival Analysis, Cox Regression
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommlsurvlrnrs_xgboost_survival_cox.qmdusingquarto::html

Last update: 2026-01-16
Started: 2024-05-29

ranger: Survival Analysis
Preprocessing | Import and Prepare Data | General Configurations | Generate Training- and Test Data | Generate Training Data Folds | Experiments | Prepare Experiments | Hyperparameter Tuning | Grid Search | Bayesian Optimization | k-Fold Cross Validation | Nested Cross Validation | Inner Grid Search | Inner Bayesian Optimization | Holdout Test Dataset Performance | Predict Outcome in Holdout Test Dataset | Evaluate Performance on Holdout Test Dataset
Rendered frommlsurvlrnrs_ranger_survival.qmdusingquarto::html

Last update: 2026-01-15
Started: 2024-05-29